Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system

In this paper, a novel fault diagnosis method based on vibration signal analysis is proposed for fault diagnosis of bearings and gears. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal into several subsequences, and a multi-entropy (ME) is proposed to make up the fusion features of the vibration signal. Secondly, an improved manifold learning algorithm, local and global preserving embedding (LGPE), is applied to compress the high-dimensional fusion feature set into a two-dimension feature set. Finally, according to the clustering accuracy of different feature set, the fault classification and diagnosis can be performed in the reduced two-dimension space. The performance of the proposed technique is tested on the fault of wind turbine transmission system. The application results indicate that the proposed method can achieve high accuracy of fault diagnosis

Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning

Deep Learning (DL) can diagnose faults and assess machine health from raw condition monitoring data without manually designed statistical features. However, practical manufacturing applications remain extremely difficult for existing DL methods. Machine data is often unlabeled and from very few health conditions (e.g., only normal operating data). Furthermore, models often encounter shifts in domain as process parameters change and new categories of faults emerge. Traditional supervised learning may struggle to learn compact, discriminative representations that generalize to these unseen target domains since it depends on having plentiful classes to partition the feature space with decision boundaries. Transfer Learning (TL) with domain adaptation attempts to adapt these models to unlabeled target domains but assumes similar underlying structure that may not be present if new faults emerge. This study proposes focusing on maximizing the feature generality on the source domain and applying TL via weight transfer to copy the model to the target domain. Specifically, Self-Supervised Learning (SSL) with Barlow Twins may produce more discriminative features for monitoring health condition than supervised learning by focusing on semantic properties of the data. Furthermore, Federated Learning (FL) for distributed training may also improve generalization by efficiently expanding the effective size and diversity of training data by sharing information across multiple client machines. Results show that Barlow Twins outperforms supervised learning in an unlabeled target domain with emerging motor faults when the source training data contains very few distinct categories. Incorporating FL may also provide a slight advantage by diffusing knowledge of health conditions between machines

회전기계 내 저해상도 및 고해상도 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 방법론

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.Rotating machinery is widely used in many industrial sites, including manufacturing and power generation. Unpredicted failures in these systems can result in huge economic and human losses. To prevent this situation, fault diagnosis studies have gathered much attention, with the goal of operating rotating machines without the occurrence of any unpredicted problems. Fault diagnosis methods aim to accurately detect any abnormality prior to failure and classify the health conditions of the target system. Recently, fault diagnosis studies using deep learning have achieved excellent performance thanks to the ability of new methods to autonomously extract meaningful features. For this purpose, two types of signals of different resolutions are measured from rotating machinery, specifically: operation signals and vibration signals. Operation signals, which are measured with a low sampling rate, are obtained in real-time and contain various types of condition parameters that enable global monitoring of the system. Vibration signals with a high sampling rate are obtained when an event occurs, not in real-time. Using these signals of different resolutions, two sub-tasks of fault diagnosis – anomaly detection and fault identification – are performed. Anomaly detection, which is conducted with operation signals, is a task to detect abnormalities in a system before those abnormalities develop into a hard failure. This is considered macro-level fault diagnosis. When performing anomaly detection, the normal data is modeled by unsupervised learning, a residual is calculated, and a threshold is determined. If the residual becomes larger than the threshold, the system is regarded as an anomaly condition. Fault identification is performed to classify the health conditions of the system using vibration signals; this is viewed as micro-level fault diagnosis. For fault identification, supervised learning is used to train a deep-learning-based classifier; thus, a large amount of labeled data is required for the training. Since fault data is insufficient in real industrial fields, data augmentation is necessary to augment the fault data. Currently, a variational auto-encoder or a generative adversarial network are the approaches most widely used for data augmentation. Anomaly detection and fault identification have been studied separately. If both tasks are integrated, macro- and micro-level fault diagnosis can be implemented. However, there are three issues that must be handled to develop a deep-learning-based methodology for macro- and micro-level fault diagnosis. First, conventional anomaly detection methods produce frequent false alarms; in other words, they may indicate a problem even if there is no anomaly in the system. This problem occurs because conventional approaches may model the normal data inadequately or set a wrong threshold; for example, one that does not consider the fluctuations in the normal data. Second, the prior generative-network-based augmentation approach has inborn limitations due to its structural properties. With this method, signals of various lengths cannot be generated because the architecture is fixed. Also, incorrect samples can be generated if the latent vectors are sampled wrongly. The final issue with health classification is that the performance of a classifier can be affected by noise in the input data. Since noise can distort the data distribution, it is difficult for a classifier to correctly classify the noisy data. Based on the current state of the field, this doctoral dissertation proposes a deep-learning-based methodology for macro- and micro-level fault diagnosis using operation and vibration signals from rotating machinery. The first research thrust proposes new methods for modeling and threshold setting to reduce false alarms related to anomaly detection. The proposed modeling method is developed by applying ensemble and denoising techniques to auto-encoders. Further, a threshold is newly proposed using the joint distribution of the output and the residual. Consequently, the proposed method considers the fluctuations in the normal data, which can significantly reduce false alarms. The second research thrust proposes a new generative network to generate signals of variable lengths. The proposed network, whose input and output are the time and amplitude, respectively, is designed to learn the frequency information of the training data. The proposed method is implemented to reflect the signal processing knowledge, including the use of the Nyquist theorem. After the training is finished, the proposed model can produce signals of various lengths in the desired time range. The proposed approach can also focus on the characteristic frequency components, thanks to attention blocks. The third research thrust proposes a novel training method that simultaneously learns the classification and denoising tasks. In the proposed scheme, multi-task learning is used to allow a classifier to solve the classification and denoising tasks concurrently. The proposed method can be applied to any deep-learning algorithm, regardless of the network type. The classifier that is trained by the proposed method can classify the health conditions, as well as remove noise in the input signals.회전기계는 제조 및 발전과 같이 다양한 산업 현장에서 널리 사용되고 있다. 회전기계의 예기치 못한 고장은 막대한 경제적, 인적 손실을 야기할 수 있다. 이러한 상황을 예방하기 위해서, 회전기계의 건전성 상태를 정확히 관리하는 것을 목표로 하는 고장 진단 연구가 주목을 받고 있다. 고장 진단 기법들은 목표 시스템의 이상을 정확히 감지하고 건전성 상태를 식별하는 것을 목표로 한다. 최근에는 딥러닝 기반 연구들이 자동적으로 유의미한 특성인자를 추출하는 능력 덕분에 뛰어난 진단 성능을 보이고 있다. 회전기계에서는 해상도가 서로 다른 운전 신호 및 진동 신호가 취득된다. 저샘플링 주파수로 취득되는 운전 신호는 실시간으로 얻어지고, 시스템을 전반적으로 관리할 수 있는 다양한 종류의 상태 변수를 포함하고 있다. 진동 신호는 고샘플링 주파수로 측정되고 실시간이 아니라, 고장이 발생하면 취득된다. 해상도가 다른 두 신호를 활용해서 고장 진단의 두 가지 하위 테스크인 이상 감지 및 고장 식별이 수행된다. 운전 신호를 가지고 수행되는 이상 감지는 시스템의 이상을 가능하면 빨리 감지하는 것을 목표로 한다. 이것은 거시적 수준의 고장 진단으로 여겨진다. 이상 감지 수행 시, 정상 데이터는 비지도 학습 방식으로 모델링 되고, 잔차 신호가 계산된 후에 기준치가 결정된다. 잔차 신호가 기준치를 초과하면, 해당 시스템은 이상이 있다고 판단된다. 고장 식별은 진동 신호를 사용해서 시스템의 건전성 상태를 분류하는 것을 목표로 한다. 이것은 미시적 수준의 고장 진단으로 여겨진다. 지도학습 방식을 활용해 딥러닝 기반 진단기를 학습시킨다. 그러므로 많은 양의 라벨 데이터가 학습에 필요하다. 실제 산업 현장에서는 고장 데이터가 부족하기 때문에, 부족한 고장 데이터를 증량하기 위한 데이터 증량 기법이 필수적이다. 최근에는 변분적 오토인코더나 적대적 생성 신경망을 활용한 증량 기법이 널리 연구되고 있다. 이상 감지와 고장 식별은 각자 따로 연구되었다. 만약 두 테스크가 통합된다면, 거시적 및 미시적 고장 진단이 수행될 수 있다. 하지만, 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 개발하는 데 해결해야 할 세 가지 문제점이 있다. 첫째, 기존 이상 감지 기법들은 시스템에 아무 이상이 없어도 오감지를 빈번하게 발생시켰다. 기존 방법들은 정상 데이터를 부정확하게 모델링하거나 기준치를 잘못 설정해서 정상 데이터에 존재하는 변동을 고려하지 못한다. 둘째, 기존 생성 신경망 기반 모델들은 구조적 특징에 기인한 한계점을 갖고 있다. 다양한 길이의 신호가 만들어질 수 없고, 잠재 벡터가 잘못 샘플링되면 잘못된 샘플이 생성될 수 있다. 건전성 분류와 관련된 마지막 이슈는 분류기의 성능이 입력 데이터의 노이즈에 영향을 받을 수 있다는 점이다. 노이즈는 데이터 분포를 왜곡할 수 있기 때문에, 분류기가 노이즈가 있는 데이터를 올바르게 분류하는 것은 어렵다. 이러한 현황을 바탕으로, 본 박사학위 논문에서는 회전기계 내 운전 및 진동 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 제안한다. 첫 번째 연구는 오감지를 줄이는 이상 감지를 위해서, 새로운 모델링 및 기준치 설정 기법들을 제안한다. 제안하는 모델링 방법은 오토인코더에 앙상블 및 디노이징 기법을 적용하여 개발됐다. 또한, 결과값과 잔차 신호 사이의 결합분포를 사용해서 동적 기준치를 설정하는 기법도 개발됐다. 이를 통해, 제안하는 방법은 정상 데이터의 변동을 고려하여 오감지를 상당히 줄일 수 있다. 두 번째 연구에서는 다양한 길이의 신호를 만들기 위한 새로운 생성 모델을 제안한다. 제안하는 네트워크는 입력과 출력이 시간 및 진폭이고, 학습 데이터의 주파수 정보를 학습하도록 설계됐다. 제안하는 모델은 나이키스트 이론과 같은 신호 처리 지식을 반영하기 위해서 신중히 설계됐다. 학습 후에, 제안하는 방법은 원하는 시간대의 다양한 길이의 신호를 만들 수 있다. 또한, 제안하는 네트워크는 어텐션 블록 덕분에 특성 주파수 성분에 집중할 수 있다. 세 번째 연구는 분류와 디노이징 테스크를 동시에 배우는 학습 기법을 제안한다. 제안하는 기법에서는 두 가지 테스크를 동시에 학습하기 위해서 다중 테스크 학습 기법이 사용된다. 제안하는 기법은 네트워크 종류에 상관없이 어떠한 딥러닝 알고리즘에 적용될 수 있다. 제안하는 방법으로 학습된 분류기는 건전성 상태를 잘 분류할 뿐만 아니라, 입력 신호의 노이즈도 제거할 수 있다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 5 1.3 Dissertation Layout 9 Chapter 2 Technical Background and Literature Review 10 2.1 Fault Diagnosis Methods of Rotating Machinery 10 2.2 Low- and High-resolution Signals from Rotating Machinery 13 2.3 Review of Deep Learning Algorithms 15 2.3.1 One-dimensional Convolutional Neural Network (1D CNN) 16 2.3.2 Long Short-term Memory (LSTM) 17 2.4 Deep-learning-based Macro- and Micro-level Fault Diagnosis Methods 19 2.4.1 Anomaly Detection 23 2.4.2 Data Augmentation 28 2.4.3 Health Classification 32 2.5 Summary and Discussion 35 Chapter 3 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) for Anomaly Detection 37 3.1 Background: Deep-learning-based Anomaly Detection 39 3.1.1 Conventional Methods to Model the Normal Data 39 3.1.2 Conventional Methods to Set a Threshold 41 3.2 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) 42 3.3 Performance Evaluation Metrics 47 3.4 Description of the Validation Datasets 50 3.5 Validation of the Proposed Method 58 3.5.1 Case Study 1: Dataset A1 58 3.5.2 Case Study 2: Dataset A2 74 3.5.3 Analysis and Discussion 89 3.6 Summary and Discussion 95 Chapter 4 Frequency-learning Generative Network (FLGN) for Data Augmentation 96 4.1 Background: Fourier Series 97 4.2 Frequency-learning Generative Network (FLGN) 99 4.2.1 Problem Formulation 99 4.2.2 Overall Procedure of FLGN 100 4.2.3 Deep-learning Implementation Details to Reflect Signals Processing Knowledge 105 4.3 Experimental Implementation Setting 106 4.3.1 Hyper-parameter Setting 107 4.3.2 Evaluation Scheme 107 4.4 Description of the Validation Datasets 111 4.5 Validation of the Proposed Method 119 4.5.1 Case Study 1: Simulated Signal 119 4.5.2 Case Study 2: RK4 Testbed Dataset 128 4.5.3 Case Study 3: MAFAULDA 141 4.5.4 Analysis and Discussion 153 4.6 Summary and Discussion 158 Chapter 5 Multi-task Learning of Classification and Denoising (MLCD) for Health Classification 159 5.1 Background: Multi-task Learning 160 5.2 Multi-task Learning of Classification and Denoising (MLCD) 161 5.2.1 Overall Procedure of MLCD 162 5.2.2 Integration with LSTM: MLCD-LSTM 165 5.2.3 Integration with 1D CNN: MLCD-1D CNN 166 5.3 Preprocessing Techniques 170 5.4 Description of the Validation Datasets 172 5.5 Validation of the Proposed Method 176 5.5.1 Case Study 1: MLCD-LSTM 176 5.5.2 Case Study 2: MLCD-1D CNN 183 5.6 Summary and Discussion 190 Chapter 6 Conclusion 191 6.1 Contributions and Significance 191 6.2 Suggestions for Future Research 194 References 196 국문 초록 209박

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline

This study proposes a framework for the automated hyperparameter optimization of a bearing fault detection pipeline for permanent magnet synchronous motors (PMSMs) without the need of external sensors. A automated machine learning (AutoML) pipeline search is performed by means of a genetic optimization to reduce human induced bias due to inappropriate parameterizations. For this purpose, a search space is defined, which includes general methods of signal processing and manipulation as well as methods tailored to the respective task and domain. The proposed framework is evaluated on the bearing fault detection use case under real world conditions. Considerations on the generalization of the deployed fault detection pipelines are also taken into account. Likewise, attention was paid to experimental studies for evaluations of the robustness of the fault detection pipeline to variations of the motors working condition parameters between the training and test domain. The present work contributes to the research of fault detection on rotating machinery in the following terms: (1) Reduction of the human induced bias to the data science process, while still considering expert and task related knowledge, ending in a generic search approach (2) tackling the bearing fault detection task without the need for external sensors (sensorless) (3) learning a domain robust fault detection pipeline applicable to varying motor operating parameters without the need of re-parameterizations or fine-tuning (4) investigations on working condition discrepancies with an excessive degree to determine the pipeline limitations regarding the abstraction of the motor parameters and the pipeline hyperparametersComment: 8 pages, 4 figures, 5 tables, ieee conference paper template use

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry

The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the rate of return by predicting failures before incidents. Digitalization has taken over the world with advances in sensor technology, wireless communication and computational capabilities, however oil & gas industry has not taken full advantage of this despite being technology centric. Dynamic seals are a vital part of reciprocating and rotary equipment such as compressor, pumps, engines, etc. and are considered most frequently failing component. Polymeric seals are increasingly complex and non-linear in behavior and have been the research of interest since 1950s. Most of the prognostic studies on seals are physics-based and requires direct estimation of different physical parameters to assess the degradation of seals, which are often difficult to obtain during operation. Another feasible approach to predict the failure is from performance related sensor data and is termed as data-driven prognostics. The offline phase of this approach is where the performance related data from the component of interest are acquired, pre-processed and artificial intelligence tools or statistical methods are used to model the degradation of a system. The developed models are then deployed online for a real-time condition monitoring. There is a lack of research on the data-driven based tools and methods for dynamic seal prognosis. The primary goal in this dissertation is to develop offline data-driven intelligent condition monitoring and prognostic methods for two types of dynamic seals used in the oil & gas industry, to avoid fatal breakdown of rotary and reciprocating equipment. Accordingly, the interest in this dissertation lies in developing models to effectively evaluate and classify the running condition of rotary seals; assess the progression of degradation from its incipient to failure and to estimate the remaining useful life (RUL) of reciprocating seals. First, a data-driven prognostic framework is developed to classify the running condition of rotary seals. An accelerated aging and testing procedure simulating rotary seal operation in oil field is developed to capture the behavior of seals through their cycle of operation until failure. The diagnostic capability of torque, leakage and vibration signal in differentiating the health states of rotary seals using experiments are compared. Since the key features that differentiate the health condition of rotary seals are unknown, an extensive feature extraction in time and frequency domain is carried out and a wrapper-based feature selection approach is used to select relevant features, with Multilayer Perceptron neural network utilized as classification technique. The proposed approach has shown that features extracted from torque and leakage lack a better discriminating power on its own, in classifying the running condition of seals throughout its service life. The classifier built using optimal set of features from torque and leakage collectively has resulted in a high classification accuracy when compared to random forest and logistic regression, even for the data collected at a different operating condition. Second, a data-driven approach to predict the degradation process of reciprocating seals based on friction force signal using a hybrid Particle Swarm Optimization - Support Vector Machine is presented. There is little to no knowledge on the feature that reflects the degradation of reciprocating seals and on the application of SVM in predicting the future running condition of polymeric components such as seals. Controlled run-to-failure experiments are designed and performed, and data collected from a dedicated experimental set-up is used to develop the proposed approach. A degradation feature with high monotonicity is used as an indicator of seal degradation. The pseudo nearest neighbor is used to determine the essential number of inputs for forecasting the future trend. The most challenging aspect of tuning parameters in SVM is framed in terms of an optimization problem aimed at minimizing the prediction error. The results indicate the effectiveness and better accuracy of the proposed approach when compared to GA-SVM and XGBoost. Finally, a deep neural network-based approach for estimating remaining useful life of reciprocating seals, using force and leakage signals is presented. Time domain and frequency domain statistical features are extracted from the measurements. An ideal prognostic feature should be well correlated with degradation time, monotonically increasing or decreasing and robust to outliers. The identified metrics namely: monotonicity, correlation and robustness are used to evaluate the goodness of extracted features. Each of the three metric carries a relative importance in the RUL estimation and a weighted linear combination of the metrics are used to rank and select the best set of prognostic features. The redundancy in the selected features is eliminated using Kelley-Gardner-Sutcliffe penalty function-based correlation-clustering algorithm to select a representative feature from each of the clusters. Finally, RUL estimation is modeled using a deep neural network model. Run-to-failure data collected from a reciprocating set-up was used to validate this approach and the findings show that the proposed approach can improve the accuracy of RUL prediction when compared to PSO-SVM and XGBoost regression. This research has important contribution and implications to rotary and reciprocating seal domain in utilizing sensors along with machine learning algorithms in assessing the health state and prognosis of seals without any direct measurements. This research has paved the way to move from a traditional fail-and-fix to predict-and-prevent approach in maintenance of seals. The findings of this research are foundational for developing an online degradation assessment platform which can remotely monitor the performance degradation of seals and provide action recommendations on maintenance decisions. This would be of great interest to customers and oil field operators to improve equipment utilization, control maintenance cost by enabling just-in-time maintenance and increase rate of return on equipment by predicting failures before incidents

Ship machinery condition monitoring using vibration data through supervised learning

This paper aims to present an integrated methodology for the monitoring of marine machinery using vibration data. Monitoring of machinery is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. The proposed methodology will train models using pre-classified (healthy/faulty) data and then classify new data points using the models developed. For this, vibration points are first acquired, appropriately processed and stored in a database. Specific features are then extracted from the data and stored. These data are then used to train supervised models pertinent to specific machinery components. Finally, new data are compared against the models developed in order to evaluate their condition. The above will provide a flexible but robust framework for the early detection of emerging machinery faults. This will lead to minimisation of ship downtime and increase of the ship’s operability and income through operational enhancement

SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan